Approximation Properties of Laplace-Type Estimators

Kormilitsina, Anna; Nekipelov, Denis

doi:10.1108/s0731-9053(2012)0000028010

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…One must instead rely on derivative-free methods (such as the Nelder-Mead simplex method); random search algorithms (such as simulated annealing); or abandon optimization altogether, and instead implement a Laplace-type estimator, via Markov Chain Monte Carlo (MCMC; see Chernozhukov and Hong, 2003). But convergence of derivative-free methods is often very slow; while MCMC, even when it converges, may produce (in finite samples) an estimator substantially different from the optimum of the statistical criterion to which it is applied (see Kormiltsina and Nekipelov, 2012). Thus, the non-smoothness of the criterion functions that define II estimators render them very difficult to use in the case of discrete data.…”

Section: Indirect Inference For Discrete Choice Modelsmentioning

confidence: 99%

Generalized indirect inference for discrete choice models

Bruins

Duffy

Keane

et al. 2018

Journal of Econometrics

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This paper develops and implements a practical simulation-based method for estimating dynamic discrete choice models. The method, which can accommodate lagged dependent variables, serially correlated errors, unobserved variables, and many alternatives, builds on the ideas of indirect inference. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering gradientbased optimization methods useless. To overcome this obstacle, this paper shows how to smooth the objective surface. The key idea is to use a smoothed function of the latent utilities as the dependent variable in the auxiliary model. As the smoothing parameter goes to zero, this function delivers the discrete choice implied by the latent utilities, thereby guaranteeing consistency. We establish conditions on the smoothing such that our estimator enjoys the same limiting distribution as the indirect inference estimator, while at the same time ensuring that the smoothing facilitates the convergence of gradient-based optimization methods. A set of Monte Carlo experiments shows that the method is fast, robust, and nearly as efficient as maximum likelihood when the auxiliary model is sufficiently rich.

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Section: Indirect Inference For Discrete Choice Modelsmentioning

confidence: 99%

Generalized indirect inference for discrete choice models

Bruins

Duffy

Keane

et al. 2018

Journal of Econometrics

View full text Add to dashboard Cite

show abstract

“…In the business cycle literature, a wide range of values for the elasticity of substitution between different types of labor ( ) has been used. For example, Adolfson et al (2007) use the value 21 in a model calibrated for the euro area, Kormilitsina and Nekipelov (2012) use 6 and Coenen et al (2010) use 3. We set the parameter to 9, which is near the middle of the range used in the literature.…”

Section: Parameter Valuesmentioning

confidence: 99%

Fiscal Devaluation in a Monetary Union

Engler

Ganelli²,

Tervala

et al. 2017

IMF Econ Rev

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Between 1999 and the onset of the economic crisis in 2008 real exchange rates in Greece, Ireland, Italy, Portugal and Spain appreciated relative to the rest of the euro area. This divergence in competitiveness was reected in the emergence of current account imbalances. Given that exchange rate devaluations are no longer available in a monetary union, one potential way to address such imbalances is through a scal devaluation. We use a DSGE model calibrated to the euro area to investigate the impact of a scal devaluation, modeled as a revenue-neutral shift from employers' social contributions to the Value Added Tax. We nd that a scal devaluation carried out in`Southern European countries' has a strong positive eect on output, but a mild eect on the trade balance of these countries. In addition, the negative eect on`Central-Northern countries' output is weak.

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“…In the business cycle literature, a wide range of values for the elasticity of substitution between different types of labor (𝜖 𝑤 ) has been used. For example, Adolfson et al (2007) use the value 21 in a model calibrated for the euro area, Kormilitsina and Nekipelov (2012) use 6 and Coenen et al ( 2010) use 3. We set the parameter to 9, which is near the middle of the range used in the literature.…”

Section: Parameter Valuesmentioning

confidence: 99%